Overview

Dataset statistics

Number of variables28
Number of observations2637
Missing cells1247
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory577.0 KiB
Average record size in memory224.1 B

Variable types

Numeric6
Text4
Categorical18

Alerts

sim1_issue_area_score is highly imbalanced (90.3%)Imbalance
sim2_issue_area_score is highly imbalanced (91.9%)Imbalance
issue_area has 56 (2.1%) missing valuesMissing
first_party_entity has 70 (2.7%) missing valuesMissing
second_party_entity has 84 (3.2%) missing valuesMissing
issue_area_sim1_facts has 64 (2.4%) missing valuesMissing
issue_area_sim2_facts has 90 (3.4%) missing valuesMissing
issue_area_sim1_legal_question has 97 (3.7%) missing valuesMissing
issue_area_sim1_conclusion has 783 (29.7%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2024-02-19 21:58:36.133590
Analysis finished2024-02-19 21:58:42.647899
Duration6.51 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2637
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1343.419
Minimum0
Maximum2696
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:42.756915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile132.8
Q1670
median1339
Q32018
95-th percentile2556.2
Maximum2696
Range2696
Interquartile range (IQR)1348

Descriptive statistics

Standard deviation778.46236
Coefficient of variation (CV)0.57946355
Kurtosis-1.2016204
Mean1343.419
Median Absolute Deviation (MAD)674
Skewness0.0062401613
Sum3542596
Variance606003.64
MonotonicityStrictly increasing
2024-02-19T15:58:42.917910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1789 1
 
< 0.1%
1791 1
 
< 0.1%
1792 1
 
< 0.1%
1793 1
 
< 0.1%
1794 1
 
< 0.1%
1795 1
 
< 0.1%
1796 1
 
< 0.1%
1797 1
 
< 0.1%
1798 1
 
< 0.1%
Other values (2627) 2627
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2696 1
< 0.1%
2695 1
< 0.1%
2694 1
< 0.1%
2693 1
< 0.1%
2692 1
< 0.1%
2691 1
< 0.1%
2690 1
< 0.1%
2689 1
< 0.1%
2688 1
< 0.1%
2687 1
< 0.1%

term
Real number (ℝ)

Distinct66
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.0793
Minimum1955
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:43.067910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1955
5-th percentile1964
Q11984
median2000
Q32010
95-th percentile2018
Maximum2020
Range65
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.702231
Coefficient of variation (CV)0.0083675187
Kurtosis-0.63900191
Mean1996.0793
Median Absolute Deviation (MAD)12
Skewness-0.6011789
Sum5263661
Variance278.96451
MonotonicityNot monotonic
2024-02-19T15:58:43.222910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015 73
 
2.8%
2005 72
 
2.7%
2011 71
 
2.7%
2009 70
 
2.7%
2002 70
 
2.7%
1997 69
 
2.6%
1996 69
 
2.6%
1999 68
 
2.6%
2004 68
 
2.6%
1998 68
 
2.6%
Other values (56) 1939
73.5%
ValueCountFrequency (%)
1955 5
 
0.2%
1956 18
0.7%
1957 11
0.4%
1958 10
0.4%
1959 7
 
0.3%
1960 12
0.5%
1961 22
0.8%
1962 16
0.6%
1963 17
0.6%
1964 15
0.6%
ValueCountFrequency (%)
2020 42
1.6%
2019 45
1.7%
2018 66
2.5%
2017 61
2.3%
2016 58
2.2%
2015 73
2.8%
2014 61
2.3%
2013 64
2.4%
2012 63
2.4%
2011 71
2.7%

first_party
Text

Distinct2173
Distinct (%)82.4%
Missing1
Missing (%)< 0.1%
Memory size20.7 KiB
2024-02-19T15:58:43.534154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length223
Median length107
Mean length22.277314
Min length3

Characters and Unicode

Total characters58723
Distinct characters79
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2043 ?
Unique (%)77.5%

Sample

1st rowCity of Oklahoma City
2nd rowCity of Ontario, California et al.
3rd rowCity of Philadelphia et al.
4th rowCity of Philadelphia
5th rowCity of Rancho Palos Verdes, California, et al.
ValueCountFrequency (%)
et 410
 
4.5%
al 394
 
4.4%
of 371
 
4.1%
united 240
 
2.7%
states 231
 
2.6%
inc 207
 
2.3%
and 116
 
1.3%
company 86
 
1.0%
the 75
 
0.8%
corporation 62
 
0.7%
Other values (3014) 6846
75.7%
2024-02-19T15:58:44.062401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6424
 
10.9%
e 5247
 
8.9%
a 4343
 
7.4%
n 3877
 
6.6%
t 3662
 
6.2%
o 3626
 
6.2%
r 3376
 
5.7%
i 3284
 
5.6%
l 2431
 
4.1%
s 2232
 
3.8%
Other values (69) 20221
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42050
71.6%
Uppercase Letter 7887
 
13.4%
Space Separator 6424
 
10.9%
Other Punctuation 2250
 
3.8%
Dash Punctuation 62
 
0.1%
Decimal Number 41
 
0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Control 2
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5247
12.5%
a 4343
10.3%
n 3877
9.2%
t 3662
8.7%
o 3626
8.6%
r 3376
 
8.0%
i 3284
 
7.8%
l 2431
 
5.8%
s 2232
 
5.3%
c 1391
 
3.3%
Other values (18) 8581
20.4%
Uppercase Letter
ValueCountFrequency (%)
S 870
 
11.0%
C 834
 
10.6%
M 531
 
6.7%
D 417
 
5.3%
A 414
 
5.2%
L 405
 
5.1%
R 400
 
5.1%
I 397
 
5.0%
P 340
 
4.3%
J 334
 
4.2%
Other values (16) 2945
37.3%
Decimal Number
ValueCountFrequency (%)
1 8
19.5%
5 6
14.6%
9 4
9.8%
2 4
9.8%
7 4
9.8%
0 4
9.8%
3 4
9.8%
8 3
 
7.3%
4 2
 
4.9%
6 2
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1198
53.2%
, 977
43.4%
& 39
 
1.7%
' 22
 
1.0%
; 6
 
0.3%
/ 3
 
0.1%
# 2
 
0.1%
" 2
 
0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6424
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49937
85.0%
Common 8786
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5247
 
10.5%
a 4343
 
8.7%
n 3877
 
7.8%
t 3662
 
7.3%
o 3626
 
7.3%
r 3376
 
6.8%
i 3284
 
6.6%
l 2431
 
4.9%
s 2232
 
4.5%
c 1391
 
2.8%
Other values (44) 16468
33.0%
Common
ValueCountFrequency (%)
6424
73.1%
. 1198
 
13.6%
, 977
 
11.1%
- 62
 
0.7%
& 39
 
0.4%
' 22
 
0.3%
1 8
 
0.1%
; 6
 
0.1%
5 6
 
0.1%
9 4
 
< 0.1%
Other values (15) 40
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58720
> 99.9%
None 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6424
 
10.9%
e 5247
 
8.9%
a 4343
 
7.4%
n 3877
 
6.6%
t 3662
 
6.2%
o 3626
 
6.2%
r 3376
 
5.7%
i 3284
 
5.6%
l 2431
 
4.1%
s 2232
 
3.8%
Other values (66) 20218
34.4%
None
ValueCountFrequency (%)
é 1
50.0%
á 1
50.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

second_party
Text

Distinct2084
Distinct (%)79.1%
Missing1
Missing (%)< 0.1%
Memory size20.7 KiB
2024-02-19T15:58:44.460834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length193
Median length109
Mean length22.766692
Min length3

Characters and Unicode

Total characters60013
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1962 ?
Unique (%)74.4%

Sample

1st rowRose Marie Tuttle, Individually and as Administratrix of the Estate of Tuttle
2nd rowJeff Quon, et al.
3rd rowNew Jersey et al.
4th rowNew Jersey
5th rowMark J. Abrams
ValueCountFrequency (%)
et 513
 
5.5%
al 503
 
5.4%
of 430
 
4.6%
united 312
 
3.4%
states 302
 
3.3%
inc 205
 
2.2%
and 98
 
1.1%
the 81
 
0.9%
company 73
 
0.8%
state 65
 
0.7%
Other values (2907) 6708
72.2%
2024-02-19T15:58:45.042423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6664
 
11.1%
e 5293
 
8.8%
a 4644
 
7.7%
t 4010
 
6.7%
n 3849
 
6.4%
i 3671
 
6.1%
o 3622
 
6.0%
r 3208
 
5.3%
l 2567
 
4.3%
s 2384
 
4.0%
Other values (64) 20101
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43089
71.8%
Uppercase Letter 7757
 
12.9%
Space Separator 6664
 
11.1%
Other Punctuation 2374
 
4.0%
Dash Punctuation 67
 
0.1%
Decimal Number 43
 
0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5293
12.3%
a 4644
10.8%
t 4010
9.3%
n 3849
8.9%
i 3671
8.5%
o 3622
8.4%
r 3208
 
7.4%
l 2567
 
6.0%
s 2384
 
5.5%
c 1340
 
3.1%
Other values (17) 8501
19.7%
Uppercase Letter
ValueCountFrequency (%)
C 920
 
11.9%
S 877
 
11.3%
A 508
 
6.5%
M 436
 
5.6%
I 422
 
5.4%
U 375
 
4.8%
D 373
 
4.8%
B 363
 
4.7%
R 359
 
4.6%
L 332
 
4.3%
Other values (16) 2792
36.0%
Decimal Number
ValueCountFrequency (%)
1 17
39.5%
0 6
 
14.0%
3 4
 
9.3%
5 4
 
9.3%
2 3
 
7.0%
7 3
 
7.0%
9 2
 
4.7%
4 2
 
4.7%
6 1
 
2.3%
8 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 1251
52.7%
, 1033
43.5%
& 45
 
1.9%
' 25
 
1.1%
; 18
 
0.8%
/ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
6664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50846
84.7%
Common 9167
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5293
 
10.4%
a 4644
 
9.1%
t 4010
 
7.9%
n 3849
 
7.6%
i 3671
 
7.2%
o 3622
 
7.1%
r 3208
 
6.3%
l 2567
 
5.0%
s 2384
 
4.7%
c 1340
 
2.6%
Other values (43) 16258
32.0%
Common
ValueCountFrequency (%)
6664
72.7%
. 1251
 
13.6%
, 1033
 
11.3%
- 67
 
0.7%
& 45
 
0.5%
' 25
 
0.3%
; 18
 
0.2%
1 17
 
0.2%
( 9
 
0.1%
) 9
 
0.1%
Other values (11) 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60011
> 99.9%
Punctuation 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6664
 
11.1%
e 5293
 
8.8%
a 4644
 
7.7%
t 4010
 
6.7%
n 3849
 
6.4%
i 3671
 
6.1%
o 3622
 
6.0%
r 3208
 
5.3%
l 2567
 
4.3%
s 2384
 
4.0%
Other values (62) 20099
33.5%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
None
ValueCountFrequency (%)
ñ 1
100.0%

issue_area
Categorical

MISSING 

Distinct14
Distinct (%)0.5%
Missing56
Missing (%)2.1%
Memory size20.7 KiB
criminal procedure
709 
civil rights
469 
economic activity
438 
first amendment
290 
judicial power
273 
Other values (9)
402 

Length

Max length20
Median length17
Mean length14.754746
Min length6

Characters and Unicode

Total characters38082
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcivil rights
2nd rowcriminal procedure
3rd roweconomic activity
4th roweconomic activity
5th rowcivil rights

Common Values

ValueCountFrequency (%)
criminal procedure 709
26.9%
civil rights 469
17.8%
economic activity 438
16.6%
first amendment 290
11.0%
judicial power 273
 
10.4%
due process 102
 
3.9%
federalism 96
 
3.6%
privacy 59
 
2.2%
unions 46
 
1.7%
federal taxation 45
 
1.7%
Other values (4) 54
 
2.0%
(Missing) 56
 
2.1%

Length

2024-02-19T15:58:45.212422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 709
14.4%
procedure 709
14.4%
civil 469
9.6%
rights 469
9.6%
economic 438
8.9%
activity 438
8.9%
first 290
5.9%
amendment 290
5.9%
judicial 273
 
5.6%
power 273
 
5.6%
Other values (13) 552
11.2%

Most occurring characters

ValueCountFrequency (%)
i 5246
13.8%
c 3656
 
9.6%
r 3497
 
9.2%
e 3270
 
8.6%
2329
 
6.1%
o 2105
 
5.5%
t 2087
 
5.5%
a 2057
 
5.4%
n 1919
 
5.0%
m 1842
 
4.8%
Other values (13) 10074
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35753
93.9%
Space Separator 2329
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5246
14.7%
c 3656
10.2%
r 3497
9.8%
e 3270
 
9.1%
o 2105
 
5.9%
t 2087
 
5.8%
a 2057
 
5.8%
n 1919
 
5.4%
m 1842
 
5.2%
l 1631
 
4.6%
Other values (12) 8443
23.6%
Space Separator
ValueCountFrequency (%)
2329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35753
93.9%
Common 2329
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5246
14.7%
c 3656
10.2%
r 3497
9.8%
e 3270
 
9.1%
o 2105
 
5.9%
t 2087
 
5.8%
a 2057
 
5.8%
n 1919
 
5.4%
m 1842
 
5.2%
l 1631
 
4.6%
Other values (12) 8443
23.6%
Common
ValueCountFrequency (%)
2329
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5246
13.8%
c 3656
 
9.6%
r 3497
 
9.2%
e 3270
 
8.6%
2329
 
6.1%
o 2105
 
5.5%
t 2087
 
5.5%
a 2057
 
5.4%
n 1919
 
5.0%
m 1842
 
4.8%
Other values (13) 10074
26.5%

first_party_entity
Categorical

MISSING 

Distinct7
Distinct (%)0.3%
Missing70
Missing (%)2.7%
Memory size20.7 KiB
PERSON
1297 
ORG
776 
GPE
483 
CARDINAL
 
6
FAC
 
3
Other values (2)
 
2

Length

Max length8
Median length6
Mean length4.5278535
Min length3

Characters and Unicode

Total characters11623
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowGPE
2nd rowGPE
3rd rowGPE
4th rowGPE
5th rowGPE

Common Values

ValueCountFrequency (%)
PERSON 1297
49.2%
ORG 776
29.4%
GPE 483
 
18.3%
CARDINAL 6
 
0.2%
FAC 3
 
0.1%
LOC 1
 
< 0.1%
DATE 1
 
< 0.1%
(Missing) 70
 
2.7%

Length

2024-02-19T15:58:45.352424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:45.478709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
person 1297
50.5%
org 776
30.2%
gpe 483
 
18.8%
cardinal 6
 
0.2%
fac 3
 
0.1%
loc 1
 
< 0.1%
date 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 2079
17.9%
O 2074
17.8%
E 1781
15.3%
P 1780
15.3%
N 1303
11.2%
S 1297
11.2%
G 1259
10.8%
A 16
 
0.1%
C 10
 
0.1%
D 7
 
0.1%
Other values (4) 17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11623
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2079
17.9%
O 2074
17.8%
E 1781
15.3%
P 1780
15.3%
N 1303
11.2%
S 1297
11.2%
G 1259
10.8%
A 16
 
0.1%
C 10
 
0.1%
D 7
 
0.1%
Other values (4) 17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 11623
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2079
17.9%
O 2074
17.8%
E 1781
15.3%
P 1780
15.3%
N 1303
11.2%
S 1297
11.2%
G 1259
10.8%
A 16
 
0.1%
C 10
 
0.1%
D 7
 
0.1%
Other values (4) 17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2079
17.9%
O 2074
17.8%
E 1781
15.3%
P 1780
15.3%
N 1303
11.2%
S 1297
11.2%
G 1259
10.8%
A 16
 
0.1%
C 10
 
0.1%
D 7
 
0.1%
Other values (4) 17
 
0.1%

second_party_entity
Categorical

MISSING 

Distinct9
Distinct (%)0.4%
Missing84
Missing (%)3.2%
Memory size20.7 KiB
PERSON
1179 
ORG
734 
GPE
622 
CARDINAL
 
4
LOC
 
4
Other values (4)
 
10

Length

Max length8
Median length3
Mean length4.3979632
Min length3

Characters and Unicode

Total characters11228
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPERSON
2nd rowPERSON
3rd rowGPE
4th rowGPE
5th rowPERSON

Common Values

ValueCountFrequency (%)
PERSON 1179
44.7%
ORG 734
27.8%
GPE 622
23.6%
CARDINAL 4
 
0.2%
LOC 4
 
0.2%
DATE 4
 
0.2%
NORP 4
 
0.2%
FAC 1
 
< 0.1%
ORDINAL 1
 
< 0.1%
(Missing) 84
 
3.2%

Length

2024-02-19T15:58:45.621704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:45.776715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
person 1179
46.2%
org 734
28.8%
gpe 622
24.4%
cardinal 4
 
0.2%
loc 4
 
0.2%
date 4
 
0.2%
norp 4
 
0.2%
fac 1
 
< 0.1%
ordinal 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 1922
17.1%
O 1922
17.1%
P 1805
16.1%
E 1805
16.1%
G 1356
12.1%
N 1188
10.6%
S 1179
10.5%
A 14
 
0.1%
C 9
 
0.1%
D 9
 
0.1%
Other values (4) 19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11228
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1922
17.1%
O 1922
17.1%
P 1805
16.1%
E 1805
16.1%
G 1356
12.1%
N 1188
10.6%
S 1179
10.5%
A 14
 
0.1%
C 9
 
0.1%
D 9
 
0.1%
Other values (4) 19
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 11228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1922
17.1%
O 1922
17.1%
P 1805
16.1%
E 1805
16.1%
G 1356
12.1%
N 1188
10.6%
S 1179
10.5%
A 14
 
0.1%
C 9
 
0.1%
D 9
 
0.1%
Other values (4) 19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1922
17.1%
O 1922
17.1%
P 1805
16.1%
E 1805
16.1%
G 1356
12.1%
N 1188
10.6%
S 1179
10.5%
A 14
 
0.1%
C 9
 
0.1%
D 9
 
0.1%
Other values (4) 19
 
0.2%

judges
Text

Distinct487
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:45.996068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length171
Median length167
Mean length116.05612
Min length12

Characters and Unicode

Total characters306040
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)15.9%

Sample

1st rowThurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens;Sandra Day O'Connor
2nd rowJohn G. Roberts, Jr.;John Paul Stevens;Antonin Scalia;Anthony M. Kennedy;Clarence Thomas;Ruth Bader Ginsburg;Stephen G. Breyer;Samuel A. Alito, Jr.;Sonia Sotomayor
3rd rowPotter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens
4th rowPotter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens
5th rowWilliam H. Rehnquist
ValueCountFrequency (%)
g 1972
 
5.6%
a 1599
 
4.5%
h 1519
 
4.3%
m 1348
 
3.8%
roberts 985
 
2.8%
alito 963
 
2.7%
bader 945
 
2.7%
j 891
 
2.5%
john 841
 
2.4%
brennan 840
 
2.4%
Other values (308) 23494
66.4%
2024-02-19T15:58:46.447557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32760
 
10.7%
n 21424
 
7.0%
a 20461
 
6.7%
r 20232
 
6.6%
e 20195
 
6.6%
o 15023
 
4.9%
l 14838
 
4.8%
; 14492
 
4.7%
. 13254
 
4.3%
i 12390
 
4.0%
Other values (40) 120971
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 191497
62.6%
Uppercase Letter 50455
 
16.5%
Space Separator 32760
 
10.7%
Other Punctuation 31328
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 21424
11.2%
a 20461
10.7%
r 20232
10.6%
e 20195
10.5%
o 15023
7.8%
l 14838
7.7%
i 12390
 
6.5%
t 12290
 
6.4%
h 9942
 
5.2%
u 8190
 
4.3%
Other values (15) 36512
19.1%
Uppercase Letter
ValueCountFrequency (%)
J 6204
12.3%
S 6102
12.1%
B 5213
10.3%
A 4466
8.9%
W 4064
 
8.1%
R 4030
 
8.0%
G 3214
 
6.4%
H 2714
 
5.4%
M 2042
 
4.0%
T 1852
 
3.7%
Other values (10) 10554
20.9%
Other Punctuation
ValueCountFrequency (%)
; 14492
46.3%
. 13254
42.3%
, 3263
 
10.4%
' 319
 
1.0%
Space Separator
ValueCountFrequency (%)
32760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 241952
79.1%
Common 64088
 
20.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 21424
 
8.9%
a 20461
 
8.5%
r 20232
 
8.4%
e 20195
 
8.3%
o 15023
 
6.2%
l 14838
 
6.1%
i 12390
 
5.1%
t 12290
 
5.1%
h 9942
 
4.1%
u 8190
 
3.4%
Other values (35) 86967
35.9%
Common
ValueCountFrequency (%)
32760
51.1%
; 14492
22.6%
. 13254
20.7%
, 3263
 
5.1%
' 319
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32760
 
10.7%
n 21424
 
7.0%
a 20461
 
6.7%
r 20232
 
6.6%
e 20195
 
6.6%
o 15023
 
4.9%
l 14838
 
4.8%
; 14492
 
4.7%
. 13254
 
4.3%
i 12390
 
4.0%
Other values (40) 120971
39.5%

lower_court
Text

Distinct79
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:46.716558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length67
Median length55
Mean length34.865757
Min length13

Characters and Unicode

Total characters91941
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.5%

Sample

1st rowUnited States Court of Appeals for the Tenth Circuit
2nd rowUnited States Court of Appeals for the Ninth Circuit
3rd rowNew Jersey Supreme Court
4th rowNew Jersey Supreme Court
5th rowJohn Paul Stevens
ValueCountFrequency (%)
court 1832
11.8%
of 1378
 
8.8%
appeals 1214
 
7.8%
the 1173
 
7.5%
for 1173
 
7.5%
united 1173
 
7.5%
states 1173
 
7.5%
circuit 1168
 
7.5%
paul 685
 
4.4%
john 685
 
4.4%
Other values (103) 3930
25.2%
2024-02-19T15:58:47.127419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12947
14.1%
t 10373
 
11.3%
e 8443
 
9.2%
o 6158
 
6.7%
r 5676
 
6.2%
i 4859
 
5.3%
u 4216
 
4.6%
a 3901
 
4.2%
n 3706
 
4.0%
s 3697
 
4.0%
Other values (41) 27965
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67218
73.1%
Space Separator 12947
 
14.1%
Uppercase Letter 11659
 
12.7%
Other Punctuation 117
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 10373
15.4%
e 8443
12.6%
o 6158
9.2%
r 5676
8.4%
i 4859
 
7.2%
u 4216
 
6.3%
a 3901
 
5.8%
n 3706
 
5.5%
s 3697
 
5.5%
h 2939
 
4.4%
Other values (15) 13250
19.7%
Uppercase Letter
ValueCountFrequency (%)
C 3024
25.9%
S 2449
21.0%
A 1293
11.1%
U 1174
 
10.1%
P 977
 
8.4%
J 696
 
6.0%
N 574
 
4.9%
F 339
 
2.9%
L 305
 
2.6%
E 161
 
1.4%
Other values (13) 667
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 116
99.1%
' 1
 
0.9%
Space Separator
ValueCountFrequency (%)
12947
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78877
85.8%
Common 13064
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 10373
13.2%
e 8443
 
10.7%
o 6158
 
7.8%
r 5676
 
7.2%
i 4859
 
6.2%
u 4216
 
5.3%
a 3901
 
4.9%
n 3706
 
4.7%
s 3697
 
4.7%
C 3024
 
3.8%
Other values (38) 24824
31.5%
Common
ValueCountFrequency (%)
12947
99.1%
. 116
 
0.9%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12947
14.1%
t 10373
 
11.3%
e 8443
 
9.2%
o 6158
 
6.7%
r 5676
 
6.2%
i 4859
 
5.3%
u 4216
 
4.6%
a 3901
 
4.2%
n 3706
 
4.0%
s 3697
 
4.0%
Other values (41) 27965
30.4%

sim1_facts_score
Real number (ℝ)

Distinct2530
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89160303
Minimum0.84362702
Maximum0.99659427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:47.427678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.84362702
5-th percentile0.86900415
Q10.88149992
median0.8888158
Q30.89901862
95-th percentile0.92211692
Maximum0.99659427
Range0.15296724
Interquartile range (IQR)0.0175187

Descriptive statistics

Standard deviation0.016988817
Coefficient of variation (CV)0.019054239
Kurtosis3.8986702
Mean0.89160303
Median Absolute Deviation (MAD)0.0088066431
Skewness1.2705239
Sum2351.1572
Variance0.0002886199
MonotonicityNot monotonic
2024-02-19T15:58:47.591671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8871039175 2
 
0.1%
0.8922133754 2
 
0.1%
0.9251542887 2
 
0.1%
0.9101727434 2
 
0.1%
0.8861770139 2
 
0.1%
0.900665841 2
 
0.1%
0.9111162449 2
 
0.1%
0.8995667101 2
 
0.1%
0.8883341262 2
 
0.1%
0.9036394598 2
 
0.1%
Other values (2520) 2617
99.2%
ValueCountFrequency (%)
0.843627024 1
< 0.1%
0.8452467235 1
< 0.1%
0.846346046 1
< 0.1%
0.8479327905 1
< 0.1%
0.8479808064 1
< 0.1%
0.8483096118 1
< 0.1%
0.8484194327 1
< 0.1%
0.8490409204 1
< 0.1%
0.8492937241 1
< 0.1%
0.8496435842 1
< 0.1%
ValueCountFrequency (%)
0.9965942656 1
< 0.1%
0.9965942066 1
< 0.1%
0.9849692701 2
0.1%
0.9697094088 1
< 0.1%
0.9697093002 1
< 0.1%
0.9691212609 2
0.1%
0.9638260523 1
< 0.1%
0.9638259458 1
< 0.1%
0.9582655235 2
0.1%
0.9571201762 2
0.1%

sim1_issue_area_score
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
2580 
0.8415916808451746
 
56
0.8693249002079472
 
1

Length

Max length18
Median length3
Mean length3.3242321
Min length3

Characters and Unicode

Total characters8766
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2580
97.8%
0.8415916808451746 56
 
2.1%
0.8693249002079472 1
 
< 0.1%

Length

2024-02-19T15:58:47.759685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:47.880683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2580
97.8%
0.8415916808451746 56
 
2.1%
0.8693249002079472 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 2748
31.3%
0 2696
30.8%
. 2637
30.1%
4 170
 
1.9%
8 169
 
1.9%
6 113
 
1.3%
5 112
 
1.3%
9 59
 
0.7%
7 58
 
0.7%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6129
69.9%
Other Punctuation 2637
30.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2748
44.8%
0 2696
44.0%
4 170
 
2.8%
8 169
 
2.8%
6 113
 
1.8%
5 112
 
1.8%
9 59
 
1.0%
7 58
 
0.9%
2 3
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2748
31.3%
0 2696
30.8%
. 2637
30.1%
4 170
 
1.9%
8 169
 
1.9%
6 113
 
1.3%
5 112
 
1.3%
9 59
 
0.7%
7 58
 
0.7%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2748
31.3%
0 2696
30.8%
. 2637
30.1%
4 170
 
1.9%
8 169
 
1.9%
6 113
 
1.3%
5 112
 
1.3%
9 59
 
0.7%
7 58
 
0.7%
2 3
 
< 0.1%

sim1_legal_question_score
Real number (ℝ)

Distinct1769
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88678518
Minimum0.82841368
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:48.005914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.82841368
5-th percentile0.84308677
Q10.84308677
median0.89244223
Q30.91036254
95-th percentile0.94079344
Maximum1
Range0.17158632
Interquartile range (IQR)0.067275768

Descriptive statistics

Standard deviation0.034496019
Coefficient of variation (CV)0.038900085
Kurtosis-0.59022255
Mean0.88678518
Median Absolute Deviation (MAD)0.023834719
Skewness0.15843652
Sum2338.4525
Variance0.0011899753
MonotonicityNot monotonic
2024-02-19T15:58:48.182903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8430867724 685
 
26.0%
0.8434225194 57
 
2.2%
0.8376631349 26
 
1.0%
0.8448282712 22
 
0.8%
1 10
 
0.4%
0.8323312859 5
 
0.2%
0.956842553 2
 
0.1%
0.9070883089 2
 
0.1%
0.916862311 2
 
0.1%
0.838503288 2
 
0.1%
Other values (1759) 1824
69.2%
ValueCountFrequency (%)
0.8284136751 2
 
0.1%
0.8308549723 1
 
< 0.1%
0.8323312859 5
 
0.2%
0.8351272384 2
 
0.1%
0.8376631349 26
 
1.0%
0.838503288 2
 
0.1%
0.8419100297 1
 
< 0.1%
0.8430867724 685
26.0%
0.8434225194 57
 
2.2%
0.8448282712 22
 
0.8%
ValueCountFrequency (%)
1 10
0.4%
0.9945682111 2
 
0.1%
0.9838837398 1
 
< 0.1%
0.9795666875 1
 
< 0.1%
0.9795664635 1
 
< 0.1%
0.9752808409 1
 
< 0.1%
0.9752807304 1
 
< 0.1%
0.9751341933 1
 
< 0.1%
0.9751341381 1
 
< 0.1%
0.974998752 1
 
< 0.1%

sim1_conclusion_score
Real number (ℝ)

Distinct1770
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88437882
Minimum0.82841368
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:48.415905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.82841368
5-th percentile0.82841368
Q10.82841368
median0.8989985
Q30.91140618
95-th percentile0.92821138
Maximum1
Range0.17158632
Interquartile range (IQR)0.082992505

Descriptive statistics

Standard deviation0.038220882
Coefficient of variation (CV)0.043217772
Kurtosis-0.88205005
Mean0.88437882
Median Absolute Deviation (MAD)0.016482078
Skewness-0.3774067
Sum2332.1069
Variance0.0014608358
MonotonicityNot monotonic
2024-02-19T15:58:48.630914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8284136751 684
 
25.9%
0.8448282712 57
 
2.2%
0.8434225194 26
 
1.0%
0.8430867724 21
 
0.8%
1 8
 
0.3%
0.8370419915 5
 
0.2%
0.8353766446 4
 
0.2%
0.9124869046 2
 
0.1%
0.9171361711 2
 
0.1%
0.9291346981 2
 
0.1%
Other values (1760) 1826
69.2%
ValueCountFrequency (%)
0.8284136751 684
25.9%
0.8309856734 1
 
< 0.1%
0.8353766446 4
 
0.2%
0.8370419915 5
 
0.2%
0.8376631349 2
 
0.1%
0.838503288 1
 
< 0.1%
0.8430867724 21
 
0.8%
0.8434225194 26
 
1.0%
0.8448282712 57
 
2.2%
0.8522301606 2
 
0.1%
ValueCountFrequency (%)
1 8
0.3%
0.9946429176 1
 
< 0.1%
0.9946428003 1
 
< 0.1%
0.9918855266 2
 
0.1%
0.9894521524 2
 
0.1%
0.9811925983 2
 
0.1%
0.9784747084 1
 
< 0.1%
0.9737086382 2
 
0.1%
0.9720424785 1
 
< 0.1%
0.9720423691 1
 
< 0.1%

sim2_facts_score
Real number (ℝ)

Distinct2600
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88478126
Minimum0.8419191
Maximum0.95339013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2024-02-19T15:58:48.795914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.8419191
5-th percentile0.86540007
Q10.87714649
median0.88394216
Q30.8916227
95-th percentile0.90649236
Maximum0.95339013
Range0.11147103
Interquartile range (IQR)0.014476212

Descriptive statistics

Standard deviation0.012749114
Coefficient of variation (CV)0.01440934
Kurtosis2.1982335
Mean0.88478126
Median Absolute Deviation (MAD)0.007229054
Skewness0.50144187
Sum2333.1682
Variance0.0001625399
MonotonicityNot monotonic
2024-02-19T15:58:48.953905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9193496215 2
 
0.1%
0.8892169443 2
 
0.1%
0.8945632823 2
 
0.1%
0.885854904 2
 
0.1%
0.8864592163 2
 
0.1%
0.9030608886 2
 
0.1%
0.8864475309 2
 
0.1%
0.8959104881 2
 
0.1%
0.8816426878 2
 
0.1%
0.8952355858 2
 
0.1%
Other values (2590) 2617
99.2%
ValueCountFrequency (%)
0.8419190974 1
< 0.1%
0.843472046 1
< 0.1%
0.8443548906 1
< 0.1%
0.8444856657 1
< 0.1%
0.8449577965 1
< 0.1%
0.8453457857 1
< 0.1%
0.8463301172 1
< 0.1%
0.8467016109 1
< 0.1%
0.8472788971 1
< 0.1%
0.847486475 1
< 0.1%
ValueCountFrequency (%)
0.953390131 1
< 0.1%
0.9494333749 1
< 0.1%
0.949433324 1
< 0.1%
0.9491772639 1
< 0.1%
0.9440868236 1
< 0.1%
0.9429398617 1
< 0.1%
0.9429397621 1
< 0.1%
0.9423166098 1
< 0.1%
0.9360227789 1
< 0.1%
0.9320397459 1
< 0.1%

sim2_issue_area_score
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
2578 
0.8397775851897517
 
56
0.8869493300835362
 
2
0.8693249002079472
 
1

Length

Max length18
Median length3
Mean length3.3356086
Min length3

Characters and Unicode

Total characters8796
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2578
97.8%
0.8397775851897517 56
 
2.1%
0.8869493300835362 2
 
0.1%
0.8693249002079472 1
 
< 0.1%

Length

2024-02-19T15:58:49.132915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:49.253914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2578
97.8%
0.8397775851897517 56
 
2.1%
0.8869493300835362 2
 
0.1%
0.8693249002079472 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 2690
30.6%
0 2644
30.1%
. 2637
30.0%
7 282
 
3.2%
8 175
 
2.0%
5 170
 
1.9%
9 119
 
1.4%
3 65
 
0.7%
6 5
 
0.1%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6159
70.0%
Other Punctuation 2637
30.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2690
43.7%
0 2644
42.9%
7 282
 
4.6%
8 175
 
2.8%
5 170
 
2.8%
9 119
 
1.9%
3 65
 
1.1%
6 5
 
0.1%
2 5
 
0.1%
4 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2690
30.6%
0 2644
30.1%
. 2637
30.0%
7 282
 
3.2%
8 175
 
2.0%
5 170
 
1.9%
9 119
 
1.4%
3 65
 
0.7%
6 5
 
0.1%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2690
30.6%
0 2644
30.1%
. 2637
30.0%
7 282
 
3.2%
8 175
 
2.0%
5 170
 
1.9%
9 119
 
1.4%
3 65
 
0.7%
6 5
 
0.1%
2 5
 
0.1%

fpw
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
1740 
0.0
897 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1740
66.0%
0.0 897
34.0%

Length

2024-02-19T15:58:49.387919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:49.516077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1740
66.0%
0.0 897
34.0%

Most occurring characters

ValueCountFrequency (%)
0 3534
44.7%
. 2637
33.3%
1 1740
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3534
67.0%
1 1740
33.0%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3534
44.7%
. 2637
33.3%
1 1740
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3534
44.7%
. 2637
33.3%
1 1740
22.0%

fpw_sim1_facts
Categorical

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size20.7 KiB
1.0
1732 
0.0
904 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7908
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1732
65.7%
0.0 904
34.3%
(Missing) 1
 
< 0.1%

Length

2024-02-19T15:58:49.641082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:49.753084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1732
65.7%
0.0 904
34.3%

Most occurring characters

ValueCountFrequency (%)
0 3540
44.8%
. 2636
33.3%
1 1732
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5272
66.7%
Other Punctuation 2636
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3540
67.1%
1 1732
32.9%
Other Punctuation
ValueCountFrequency (%)
. 2636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3540
44.8%
. 2636
33.3%
1 1732
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3540
44.8%
. 2636
33.3%
1 1732
21.9%

issue_area_sim1_facts
Categorical

MISSING 

Distinct14
Distinct (%)0.5%
Missing64
Missing (%)2.4%
Memory size20.7 KiB
criminal procedure
759 
economic activity
462 
civil rights
458 
judicial power
268 
first amendment
258 
Other values (9)
368 

Length

Max length18
Median length16
Mean length14.90789
Min length6

Characters and Unicode

Total characters38358
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowjudicial power
2nd rowcriminal procedure
3rd roweconomic activity
4th roweconomic activity
5th rowfederalism

Common Values

ValueCountFrequency (%)
criminal procedure 759
28.8%
economic activity 462
17.5%
civil rights 458
17.4%
judicial power 268
 
10.2%
first amendment 258
 
9.8%
federalism 103
 
3.9%
due process 67
 
2.5%
privacy 51
 
1.9%
federal taxation 47
 
1.8%
unions 47
 
1.8%
Other values (4) 53
 
2.0%
(Missing) 64
 
2.4%

Length

2024-02-19T15:58:49.884082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 759
15.5%
procedure 759
15.5%
economic 462
9.4%
activity 462
9.4%
civil 458
9.4%
rights 458
9.4%
judicial 268
 
5.5%
power 268
 
5.5%
first 258
 
5.3%
amendment 258
 
5.3%
Other values (13) 485
9.9%

Most occurring characters

ValueCountFrequency (%)
i 5345
13.9%
c 3768
 
9.8%
r 3564
 
9.3%
e 3268
 
8.5%
2322
 
6.1%
o 2167
 
5.6%
a 2097
 
5.5%
t 2061
 
5.4%
n 1932
 
5.0%
m 1859
 
4.8%
Other values (13) 9975
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36036
93.9%
Space Separator 2322
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5345
14.8%
c 3768
10.5%
r 3564
9.9%
e 3268
 
9.1%
o 2167
 
6.0%
a 2097
 
5.8%
t 2061
 
5.7%
n 1932
 
5.4%
m 1859
 
5.2%
l 1671
 
4.6%
Other values (12) 8304
23.0%
Space Separator
ValueCountFrequency (%)
2322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36036
93.9%
Common 2322
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5345
14.8%
c 3768
10.5%
r 3564
9.9%
e 3268
 
9.1%
o 2167
 
6.0%
a 2097
 
5.8%
t 2061
 
5.7%
n 1932
 
5.4%
m 1859
 
5.2%
l 1671
 
4.6%
Other values (12) 8304
23.0%
Common
ValueCountFrequency (%)
2322
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5345
13.9%
c 3768
 
9.8%
r 3564
 
9.3%
e 3268
 
8.5%
2322
 
6.1%
o 2167
 
5.6%
a 2097
 
5.5%
t 2061
 
5.4%
n 1932
 
5.0%
m 1859
 
4.8%
Other values (13) 9975
26.0%

fpw_sim2_facts
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
1794 
0.0
843 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1794
68.0%
0.0 843
32.0%

Length

2024-02-19T15:58:50.007341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:50.103335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1794
68.0%
0.0 843
32.0%

Most occurring characters

ValueCountFrequency (%)
0 3480
44.0%
. 2637
33.3%
1 1794
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3480
66.0%
1 1794
34.0%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3480
44.0%
. 2637
33.3%
1 1794
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3480
44.0%
. 2637
33.3%
1 1794
22.7%

issue_area_sim2_facts
Categorical

MISSING 

Distinct14
Distinct (%)0.5%
Missing90
Missing (%)3.4%
Memory size20.7 KiB
criminal procedure
745 
civil rights
501 
economic activity
431 
judicial power
251 
first amendment
235 
Other values (9)
384 

Length

Max length20
Median length17
Mean length14.732234
Min length6

Characters and Unicode

Total characters37523
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcivil rights
2nd rowcivil rights
3rd roweconomic activity
4th roweconomic activity
5th rowfirst amendment

Common Values

ValueCountFrequency (%)
criminal procedure 745
28.3%
civil rights 501
19.0%
economic activity 431
16.3%
judicial power 251
 
9.5%
first amendment 235
 
8.9%
federalism 112
 
4.2%
unions 70
 
2.7%
due process 70
 
2.7%
privacy 53
 
2.0%
federal taxation 40
 
1.5%
Other values (4) 39
 
1.5%
(Missing) 90
 
3.4%

Length

2024-02-19T15:58:50.234335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 745
15.4%
procedure 745
15.4%
civil 501
10.4%
rights 501
10.4%
economic 431
8.9%
activity 431
8.9%
judicial 251
 
5.2%
power 251
 
5.2%
first 235
 
4.9%
amendment 235
 
4.9%
Other values (13) 497
10.3%

Most occurring characters

ValueCountFrequency (%)
i 5313
14.2%
c 3668
 
9.8%
r 3529
 
9.4%
e 3138
 
8.4%
2276
 
6.1%
o 2077
 
5.5%
a 1989
 
5.3%
t 1977
 
5.3%
n 1867
 
5.0%
m 1767
 
4.7%
Other values (13) 9922
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35247
93.9%
Space Separator 2276
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5313
15.1%
c 3668
10.4%
r 3529
10.0%
e 3138
 
8.9%
o 2077
 
5.9%
a 1989
 
5.6%
t 1977
 
5.6%
n 1867
 
5.3%
m 1767
 
5.0%
l 1669
 
4.7%
Other values (12) 8253
23.4%
Space Separator
ValueCountFrequency (%)
2276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35247
93.9%
Common 2276
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5313
15.1%
c 3668
10.4%
r 3529
10.0%
e 3138
 
8.9%
o 2077
 
5.9%
a 1989
 
5.6%
t 1977
 
5.6%
n 1867
 
5.3%
m 1767
 
5.0%
l 1669
 
4.7%
Other values (12) 8253
23.4%
Common
ValueCountFrequency (%)
2276
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5313
14.2%
c 3668
 
9.8%
r 3529
 
9.4%
e 3138
 
8.4%
2276
 
6.1%
o 2077
 
5.5%
a 1989
 
5.3%
t 1977
 
5.3%
n 1867
 
5.0%
m 1767
 
4.7%
Other values (13) 9922
26.4%

fpw_sim1_issue_area
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
1845 
0.0
792 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1845
70.0%
0.0 792
30.0%

Length

2024-02-19T15:58:50.383332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:50.485332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1845
70.0%
0.0 792
30.0%

Most occurring characters

ValueCountFrequency (%)
0 3429
43.3%
. 2637
33.3%
1 1845
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3429
65.0%
1 1845
35.0%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3429
43.3%
. 2637
33.3%
1 1845
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3429
43.3%
. 2637
33.3%
1 1845
23.3%

issue_area_sim1_issue_area
Categorical

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
criminal procedure
709 
economic activity
494 
civil rights
470 
first amendment
290 
judicial power
273 
Other values (8)
401 

Length

Max length18
Median length16
Mean length14.799393
Min length6

Characters and Unicode

Total characters39026
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcivil rights
2nd rowcriminal procedure
3rd roweconomic activity
4th roweconomic activity
5th rowcivil rights

Common Values

ValueCountFrequency (%)
criminal procedure 709
26.9%
economic activity 494
18.7%
civil rights 470
17.8%
first amendment 290
11.0%
judicial power 273
 
10.4%
due process 102
 
3.9%
federalism 96
 
3.6%
privacy 59
 
2.2%
unions 46
 
1.7%
federal taxation 45
 
1.7%
Other values (3) 53
 
2.0%

Length

2024-02-19T15:58:50.605332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 709
14.1%
procedure 709
14.1%
economic 494
9.8%
activity 494
9.8%
civil 470
9.4%
rights 470
9.4%
first 290
5.8%
amendment 290
5.8%
judicial 273
 
5.4%
power 273
 
5.4%
Other values (11) 550
11.0%

Most occurring characters

ValueCountFrequency (%)
i 5415
13.9%
c 3825
 
9.8%
r 3496
 
9.0%
e 3323
 
8.5%
2385
 
6.1%
o 2216
 
5.7%
t 2196
 
5.6%
a 2111
 
5.4%
n 1973
 
5.1%
m 1898
 
4.9%
Other values (13) 10188
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36641
93.9%
Space Separator 2385
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5415
14.8%
c 3825
10.4%
r 3496
9.5%
e 3323
 
9.1%
o 2216
 
6.0%
t 2196
 
6.0%
a 2111
 
5.8%
n 1973
 
5.4%
m 1898
 
5.2%
l 1631
 
4.5%
Other values (12) 8557
23.4%
Space Separator
ValueCountFrequency (%)
2385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36641
93.9%
Common 2385
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5415
14.8%
c 3825
10.4%
r 3496
9.5%
e 3323
 
9.1%
o 2216
 
6.0%
t 2196
 
6.0%
a 2111
 
5.8%
n 1973
 
5.4%
m 1898
 
5.2%
l 1631
 
4.5%
Other values (12) 8557
23.4%
Common
ValueCountFrequency (%)
2385
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5415
13.9%
c 3825
 
9.8%
r 3496
 
9.0%
e 3323
 
8.5%
2385
 
6.1%
o 2216
 
5.7%
t 2196
 
5.6%
a 2111
 
5.4%
n 1973
 
5.1%
m 1898
 
4.9%
Other values (13) 10188
26.1%

fpw_sim2_issue_area
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
1690 
0.0
947 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1690
64.1%
0.0 947
35.9%

Length

2024-02-19T15:58:50.738343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:50.838346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1690
64.1%
0.0 947
35.9%

Most occurring characters

ValueCountFrequency (%)
0 3584
45.3%
. 2637
33.3%
1 1690
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3584
68.0%
1 1690
32.0%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3584
45.3%
. 2637
33.3%
1 1690
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3584
45.3%
. 2637
33.3%
1 1690
21.4%

issue_area_sim2_issue_area
Categorical

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
criminal procedure
709 
civil rights
470 
economic activity
438 
first amendment
290 
judicial power
273 
Other values (7)
457 

Length

Max length18
Median length16
Mean length14.581722
Min length6

Characters and Unicode

Total characters38452
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcivil rights
2nd rowcriminal procedure
3rd roweconomic activity
4th roweconomic activity
5th rowcivil rights

Common Values

ValueCountFrequency (%)
criminal procedure 709
26.9%
civil rights 470
17.8%
economic activity 438
16.6%
first amendment 290
11.0%
judicial power 273
 
10.4%
privacy 117
 
4.4%
due process 102
 
3.9%
federalism 96
 
3.6%
unions 46
 
1.7%
federal taxation 45
 
1.7%
Other values (2) 51
 
1.9%

Length

2024-02-19T15:58:50.961553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 709
14.3%
procedure 709
14.3%
civil 470
9.5%
rights 470
9.5%
economic 438
8.8%
activity 438
8.8%
first 290
5.8%
amendment 290
5.8%
power 273
 
5.5%
judicial 273
 
5.5%
Other values (9) 604
12.2%

Most occurring characters

ValueCountFrequency (%)
i 5301
13.8%
c 3713
 
9.7%
r 3552
 
9.2%
e 3265
 
8.5%
2327
 
6.1%
a 2109
 
5.5%
o 2102
 
5.5%
t 2080
 
5.4%
n 1915
 
5.0%
m 1842
 
4.8%
Other values (13) 10246
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36125
93.9%
Space Separator 2327
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5301
14.7%
c 3713
10.3%
r 3552
9.8%
e 3265
 
9.0%
a 2109
 
5.8%
o 2102
 
5.8%
t 2080
 
5.8%
n 1915
 
5.3%
m 1842
 
5.1%
l 1631
 
4.5%
Other values (12) 8615
23.8%
Space Separator
ValueCountFrequency (%)
2327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36125
93.9%
Common 2327
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5301
14.7%
c 3713
10.3%
r 3552
9.8%
e 3265
 
9.0%
a 2109
 
5.8%
o 2102
 
5.8%
t 2080
 
5.8%
n 1915
 
5.3%
m 1842
 
5.1%
l 1631
 
4.5%
Other values (12) 8615
23.8%
Common
ValueCountFrequency (%)
2327
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5301
13.8%
c 3713
 
9.7%
r 3552
 
9.2%
e 3265
 
8.5%
2327
 
6.1%
a 2109
 
5.5%
o 2102
 
5.5%
t 2080
 
5.4%
n 1915
 
5.0%
m 1842
 
4.8%
Other values (13) 10246
26.6%

fpw_sim1_legal_question
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
0.0
1339 
1.0
1298 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1339
50.8%
1.0 1298
49.2%

Length

2024-02-19T15:58:51.086553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:51.183555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1339
50.8%
1.0 1298
49.2%

Most occurring characters

ValueCountFrequency (%)
0 3976
50.3%
. 2637
33.3%
1 1298
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3976
75.4%
1 1298
 
24.6%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3976
50.3%
. 2637
33.3%
1 1298
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3976
50.3%
. 2637
33.3%
1 1298
 
16.4%

issue_area_sim1_legal_question
Categorical

MISSING 

Distinct13
Distinct (%)0.5%
Missing97
Missing (%)3.7%
Memory size20.7 KiB
federalism
744 
criminal procedure
525 
economic activity
341 
civil rights
327 
first amendment
251 
Other values (8)
352 

Length

Max length18
Median length16
Mean length13.616535
Min length6

Characters and Unicode

Total characters34586
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcivil rights
2nd rowcriminal procedure
3rd roweconomic activity
4th roweconomic activity
5th rowfederalism

Common Values

ValueCountFrequency (%)
federalism 744
28.2%
criminal procedure 525
19.9%
economic activity 341
12.9%
civil rights 327
12.4%
first amendment 251
 
9.5%
judicial power 168
 
6.4%
due process 60
 
2.3%
privacy 33
 
1.3%
attorneys 27
 
1.0%
unions 26
 
1.0%
Other values (3) 38
 
1.4%
(Missing) 97
 
3.7%

Length

2024-02-19T15:58:51.312556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
federalism 744
17.6%
criminal 525
12.4%
procedure 525
12.4%
economic 341
8.0%
activity 341
8.0%
civil 327
7.7%
rights 327
7.7%
first 251
 
5.9%
amendment 251
 
5.9%
power 168
 
4.0%
Other values (11) 438
10.3%

Most occurring characters

ValueCountFrequency (%)
i 4484
13.0%
e 3770
10.9%
r 3211
 
9.3%
c 2675
 
7.7%
a 2177
 
6.3%
m 2124
 
6.1%
l 1812
 
5.2%
d 1772
 
5.1%
1698
 
4.9%
t 1617
 
4.7%
Other values (13) 9246
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32888
95.1%
Space Separator 1698
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4484
13.6%
e 3770
11.5%
r 3211
9.8%
c 2675
 
8.1%
a 2177
 
6.6%
m 2124
 
6.5%
l 1812
 
5.5%
d 1772
 
5.4%
t 1617
 
4.9%
o 1526
 
4.6%
Other values (12) 7720
23.5%
Space Separator
ValueCountFrequency (%)
1698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32888
95.1%
Common 1698
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4484
13.6%
e 3770
11.5%
r 3211
9.8%
c 2675
 
8.1%
a 2177
 
6.6%
m 2124
 
6.5%
l 1812
 
5.5%
d 1772
 
5.4%
t 1617
 
4.9%
o 1526
 
4.6%
Other values (12) 7720
23.5%
Common
ValueCountFrequency (%)
1698
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4484
13.0%
e 3770
10.9%
r 3211
 
9.3%
c 2675
 
7.7%
a 2177
 
6.3%
m 2124
 
6.1%
l 1812
 
5.2%
d 1772
 
5.1%
1698
 
4.9%
t 1617
 
4.7%
Other values (13) 9246
26.7%

fpw_sim1_conclusion
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1.0
1319 
0.0
1318 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7911
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1319
50.0%
0.0 1318
50.0%

Length

2024-02-19T15:58:51.438771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T15:58:51.536771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1319
50.0%
0.0 1318
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3955
50.0%
. 2637
33.3%
1 1319
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5274
66.7%
Other Punctuation 2637
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3955
75.0%
1 1319
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3955
50.0%
. 2637
33.3%
1 1319
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3955
50.0%
. 2637
33.3%
1 1319
 
16.7%

issue_area_sim1_conclusion
Categorical

MISSING 

Distinct12
Distinct (%)0.6%
Missing783
Missing (%)29.7%
Memory size20.7 KiB
criminal procedure
565 
civil rights
348 
economic activity
284 
first amendment
202 
judicial power
159 
Other values (7)
296 

Length

Max length18
Median length16
Mean length14.729773
Min length6

Characters and Unicode

Total characters27309
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcivil rights
2nd rowcriminal procedure
3rd rowjudicial power
4th roweconomic activity
5th rowfirst amendment

Common Values

ValueCountFrequency (%)
criminal procedure 565
21.4%
civil rights 348
13.2%
economic activity 284
 
10.8%
first amendment 202
 
7.7%
judicial power 159
 
6.0%
federalism 99
 
3.8%
due process 58
 
2.2%
unions 41
 
1.6%
privacy 40
 
1.5%
attorneys 26
 
1.0%
Other values (2) 32
 
1.2%
(Missing) 783
29.7%

Length

2024-02-19T15:58:51.654770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 565
16.2%
procedure 565
16.2%
civil 348
10.0%
rights 348
10.0%
economic 284
8.1%
activity 284
8.1%
first 202
 
5.8%
amendment 202
 
5.8%
power 159
 
4.5%
judicial 159
 
4.5%
Other values (9) 379
10.8%

Most occurring characters

ValueCountFrequency (%)
i 3758
13.8%
r 2652
 
9.7%
c 2594
 
9.5%
e 2381
 
8.7%
1641
 
6.0%
a 1457
 
5.3%
o 1449
 
5.3%
t 1422
 
5.2%
n 1393
 
5.1%
m 1359
 
5.0%
Other values (13) 7203
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25668
94.0%
Space Separator 1641
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3758
14.6%
r 2652
10.3%
c 2594
10.1%
e 2381
 
9.3%
a 1457
 
5.7%
o 1449
 
5.6%
t 1422
 
5.5%
n 1393
 
5.4%
m 1359
 
5.3%
l 1210
 
4.7%
Other values (12) 5993
23.3%
Space Separator
ValueCountFrequency (%)
1641
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25668
94.0%
Common 1641
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3758
14.6%
r 2652
10.3%
c 2594
10.1%
e 2381
 
9.3%
a 1457
 
5.7%
o 1449
 
5.6%
t 1422
 
5.5%
n 1393
 
5.4%
m 1359
 
5.3%
l 1210
 
4.7%
Other values (12) 5993
23.3%
Common
ValueCountFrequency (%)
1641
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3758
13.8%
r 2652
 
9.7%
c 2594
 
9.5%
e 2381
 
8.7%
1641
 
6.0%
a 1457
 
5.3%
o 1449
 
5.3%
t 1422
 
5.2%
n 1393
 
5.1%
m 1359
 
5.0%
Other values (13) 7203
26.4%

Interactions

2024-02-19T15:58:40.949310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:37.452319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.153552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.804559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:40.258084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:41.053310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:37.596333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.253564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.911804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:40.369085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:41.160311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:37.699420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.358559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:39.068796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:40.480324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:41.511568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:37.923559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.589550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:39.312804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:40.721320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:41.626564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.054563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:38.701564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:39.436794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-19T15:58:40.838325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-19T15:58:41.837563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-19T15:58:42.304718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0termfirst_partysecond_partyissue_areafirst_party_entitysecond_party_entityjudgeslower_courtsim1_facts_scoresim1_issue_area_scoresim1_legal_question_scoresim1_conclusion_scoresim2_facts_scoresim2_issue_area_scorefpwfpw_sim1_factsissue_area_sim1_factsfpw_sim2_factsissue_area_sim2_factsfpw_sim1_issue_areaissue_area_sim1_issue_areafpw_sim2_issue_areaissue_area_sim2_issue_areafpw_sim1_legal_questionissue_area_sim1_legal_questionfpw_sim1_conclusionissue_area_sim1_conclusion
001984.0City of Oklahoma CityRose Marie Tuttle, Individually and as Administratrix of the Estate of Tuttlecivil rightsGPEPERSONThurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens;Sandra Day O'ConnorUnited States Court of Appeals for the Tenth Circuit0.8871041.00.8826800.9109450.8805101.01.00.0judicial power1.0civil rights1.0civil rights1.0civil rights1.0civil rights1.0civil rights
112009.0City of Ontario, California et al.Jeff Quon, et al.criminal procedureGPEPERSONJohn G. Roberts, Jr.;John Paul Stevens;Antonin Scalia;Anthony M. Kennedy;Clarence Thomas;Ruth Bader Ginsburg;Stephen G. Breyer;Samuel A. Alito, Jr.;Sonia SotomayorUnited States Court of Appeals for the Ninth Circuit0.8790531.00.8893240.9107850.8776081.01.01.0criminal procedure1.0civil rights0.0criminal procedure1.0criminal procedure1.0criminal procedure0.0criminal procedure
221976.0City of Philadelphia et al.New Jersey et al.economic activityGPEGPEPotter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul StevensNew Jersey Supreme Court0.9132901.00.9139050.8920450.8651241.01.01.0economic activity0.0NaN1.0economic activity0.0economic activity1.0economic activity1.0judicial power
331977.0City of PhiladelphiaNew Jerseyeconomic activityNaNGPEPotter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul StevensNew Jersey Supreme Court0.9132901.00.9177680.9106760.8631571.01.01.0economic activity1.0economic activity1.0economic activity0.0economic activity1.0economic activity1.0economic activity
442004.0City of Rancho Palos Verdes, California, et al.Mark J. Abramscivil rightsGPEPERSONWilliam H. RehnquistJohn Paul Stevens0.8935851.00.8430870.8284140.8841231.01.01.0federalism1.0economic activity1.0civil rights1.0civil rights0.0federalism0.0NaN
551985.0City of RentonPlaytime Theatres, Inc.first amendmentGPEORGThurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens;Sandra Day O'ConnorUnited States Court of Appeals for the Ninth Circuit0.8850701.00.9350670.9145690.8716801.01.01.0first amendment1.0first amendment1.0first amendment1.0first amendment1.0first amendment1.0first amendment
661988.0City of RichmondJ. A. Croson Companycivil rightsNaNORGThurgood Marshall;William J. Brennan, Jr.;Byron R. White;Harry A. Blackmun;William H. Rehnquist;John Paul Stevens;Sandra Day O'Connor;Antonin Scalia;Anthony M. KennedyUnited States Court of Appeals for the Fourth Circuit0.8807731.00.9549510.8835650.8623871.00.01.0judicial power0.0civil rights1.0civil rights1.0civil rights0.0civil rights0.0civil rights
771985.0City of RiversideRiveraattorneysNaNPERSONThurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul Stevens;Sandra Day O'ConnorUnited States Court of Appeals for the Ninth Circuit0.8877251.00.8964710.9148910.8776361.00.00.0attorneys1.0attorneys1.0attorneys0.0attorneys1.0attorneys0.0attorneys
882004.0City of San Diego, CaliforniaJohn Roefirst amendmentGPEPERSONWilliam H. RehnquistJohn Paul Stevens0.8779651.00.8430870.8284140.8778531.01.00.0economic activity0.0first amendment1.0first amendment1.0first amendment0.0federalism0.0NaN
992004.0City of Sherrill, New YorkOneida Indian Nation of New York, et al.civil rightsGPEORGWilliam H. RehnquistJohn Paul Stevens0.8974271.00.8430870.8284140.8809011.01.01.0civil rights1.0civil rights1.0civil rights1.0civil rights0.0federalism0.0NaN
Unnamed: 0termfirst_partysecond_partyissue_areafirst_party_entitysecond_party_entityjudgeslower_courtsim1_facts_scoresim1_issue_area_scoresim1_legal_question_scoresim1_conclusion_scoresim2_facts_scoresim2_issue_area_scorefpwfpw_sim1_factsissue_area_sim1_factsfpw_sim2_factsissue_area_sim2_factsfpw_sim1_issue_areaissue_area_sim1_issue_areafpw_sim2_issue_areaissue_area_sim2_issue_areafpw_sim1_legal_questionissue_area_sim1_legal_questionfpw_sim1_conclusionissue_area_sim1_conclusion
262726872001.0ZelmanSimmons-Harrisfirst amendmentPERSONPERSONWilliam H. RehnquistJohn Paul Stevens0.8758941.00.8430870.8284140.8733351.01.01.0first amendment1.0first amendment1.0first amendment1.0first amendment0.0federalism0.0NaN
262826881970.0Zenith Radio CorporationHazeltine Research, Inc.economic activityORGORGJohn M. Harlan II;Hugo L. Black;William O. Douglas;Potter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. BlackmunUnited States Court of Appeals for the Seventh Circuit0.9046021.00.8691110.8941550.8882501.01.01.0economic activity1.0judicial power1.0economic activity0.0economic activity1.0criminal procedure1.0economic activity
262926891995.0ZichermanKorean Air Lines Companyeconomic activityPERSONORGWilliam H. RehnquistJohn Paul Stevens0.9571201.00.8430870.8284140.8858251.00.00.0economic activity1.0economic activity1.0economic activity0.0economic activity0.0federalism0.0NaN
263026902016.0James W. ZiglarAhmer Iqbal Abbasi, et. al.civil rightsPERSONPERSONAnthony M. Kennedy;John G. Roberts, Jr.;Clarence Thomas;Samuel A. Alito, Jr.;Stephen G. Breyer;Ruth Bader Ginsburg;Sonia Sotomayor;Elena Kagan;Neil GorsuchUnited States Court of Appeals for the Second Circuit0.9064481.00.8911140.9338670.8994191.01.00.0first amendment1.0civil rights1.0civil rights1.0civil rights1.0first amendment1.0criminal procedure
263126912011.0M. B. Z., By His Parents and Guardians Ari Z. Zivotofsky, et ux.Hillary Rodham Clinton, Secretary of StatemiscellaneousPERSONORGJohn G. Roberts, Jr.;Antonin Scalia;Anthony M. Kennedy;Clarence Thomas;Ruth Bader Ginsburg;Stephen G. Breyer;Samuel A. Alito, Jr.;Sonia Sotomayor;Elena KaganUnited States Court of Appeals for the District of Columbia Circuit0.9458921.00.9208640.9032160.8825381.01.00.0miscellaneous1.0civil rights0.0miscellaneous0.0miscellaneous0.0miscellaneous1.0criminal procedure
263226922014.0M. B. Z., By His Parents and Guardians, Ari Z. Zivotofsky, et ux.John Kerry, Secretary of StatemiscellaneousPERSONORGAnthony M. Kennedy;Ruth Bader Ginsburg;Stephen G. Breyer;Sonia Sotomayor;Elena Kagan;Clarence Thomas;John G. Roberts, Jr.;Samuel A. Alito, Jr.;Antonin ScaliaUnited States Court of Appeals for the District of Columbia Circuit0.9458921.00.9208640.9095600.8786671.00.01.0miscellaneous1.0civil rights0.0miscellaneous0.0miscellaneous1.0miscellaneous1.0civil rights
263326931992.0ZobrestCatalina Foothills School Districtfirst amendmentPERSONORGByron R. WhiteHarry A. Blackmun0.8961601.00.8434230.8448280.8895501.01.01.0civil rights0.0first amendment1.0first amendment1.0first amendment1.0economic activity1.0NaN
263426942015.0David A. Zubik, et al.Sylvia Burwell, Secretary of Health and Human Services, et al.first amendmentPERSONORGJohn G. Roberts, Jr.;Anthony M. Kennedy;Clarence Thomas;Ruth Bader Ginsburg;Stephen G. Breyer;Samuel A. Alito, Jr.;Sonia Sotomayor;Elena KaganUnited States Court of Appeals for the Third Circuit0.9214001.00.9093590.8964640.9145681.01.01.0judicial power0.0federal taxation1.0first amendment1.0first amendment1.0judicial power0.0unions
263526952006.0Zuni Public School District No. 89 et al.United States Department of Education et al.judicial powerORGORGJohn Paul Stevens;Antonin Scalia;Anthony M. Kennedy;David H. Souter;Clarence Thomas;Ruth Bader Ginsburg;Stephen G. Breyer;John G. Roberts, Jr.;Samuel A. Alito, Jr.United States Court of Appeals for the Tenth Circuit0.8876771.00.8768090.8939800.8758781.00.01.0civil rights0.0federal taxation1.0judicial power0.0judicial power1.0civil rights1.0unions
263626961977.0ZurcherStanford Dailycriminal procedurePERSONORGPotter Stewart;Thurgood Marshall;William J. Brennan, Jr.;Byron R. White;Warren E. Burger;Harry A. Blackmun;Lewis F. Powell, Jr.;William H. Rehnquist;John Paul StevensUnited States Court of Appeals for the Ninth Circuit0.8640541.00.9393210.9161190.8635181.01.01.0attorneys0.0criminal procedure0.0criminal procedure1.0criminal procedure1.0criminal procedure1.0criminal procedure